Underutilization of many parts of radio frequency spectrum has increased the interest in dynamic spectrum allocation. Cognitive radios have been suggested as an enabling technology for dynamic allocation of spectrum resources. Spectrum sensing used for finding free spectrum that can then be used in an opportunistic manner is a key task in cognitive radio systems. It enables agile spectrum use and interference control. Recently, there has been increasing interest on developing low complexity, robust and reliable spectrum sensing methods for detecting the presence of primary users such as cellular and WLAN subscribers, with whom the cognitive radio secondary users are obligated to avoid interfering. Primary users operate in networks that have radio resources (time and frequency) allocated by regulatory bodies. Often the individual primary user equipments (UEs) have specifically allocated radio resources for their transmissions and receptions. Cognitive radio networks use spectrum in an opportunistic manner and thus rely on spectrum sensing to find holes in the spectrum for their transmissions which will avoid interfering with the primary users. Collaborative sensing by multiple secondary users allows for mitigating the effects of propagation, e.g., shadowing and fading. Regardless of the bandwidth that the spectrum sensing task investigates, spectrum sensing must be designed to use low power so that the spectrum sensing task does not inordinately deplete the portable power supply of the mobile stations.
Some spectrum sensing algorithms exploit the cyclostationarity property of communication signals. Cyclostationarity allows for detecting communication signals even at the low signal-to-noise ratio regime. It also facilitates distinguishing among co-existing communication signals and systems. These algorithms do not require any explicit assumptions on the data or noise distributions. They are based solely on the asymptotic distributions of the cyclic correlation estimators. Nevertheless, these algorithms are not necessarily highly robust in the face of noise and interference. For example, in case the actual noise distribution has heavier tails than a normal distribution, the convergence of the classical cyclic correlation estimator slows down significantly and the performance of the algorithms deteriorates or they may even fail. In practice, a significantly larger number of observations would be needed, as compared to the case of Gaussian distributed noise, in order to achieve a similar performance level as these algorithms would attain in the presence of additive white Gaussian noise (AWGN) only.
Conventional cyclostationarity based detectors have been proposed for example in a paper by A. V. Dandawate & G. B. Giannakis, “STATISTICAL TESTS FOR PRESENCE OF CYCLOSTATIONARITY” (IEEE Transactions on Signal Processing, Vol. 42, No. 9, pp. 2355-2369, 1994). Similar subject matter is discussed in a paper by J. Lunden, V. Koivunen, A. Huttunen, H. V. Poor, entitled “SPECTRUM SENSING IN COGNITIVE RADIOS BASED ON MULTIPLE CYCLIC FREQUENCIES” (Proceedings of 2nd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, Orlando, Fla., Jul. 31-Aug.3, 2007).
These and related detection techniques based on cyclostationarity use direct sample estimates of cyclostationary statistics. Hence, they are not robust in the face of heavy-tailed noise or interference. One attempt to improve the robustness is described in a paper by T. E. Biedka, L. Mili, J. H. Reed, entitled “ROBUST ESTIMATION OF CYCLIC CORRELATION IN CONTAMINATED GAUSSIAN NOISE” (Proceedings of 29th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, Calif., pp. 511-515, 1995). This paper describes using the M-estimation principle. M-estimation is a semi-parametric estimation technique that makes assumptions on the family of distribution for noise but allows small departures from the exact probability model. M-estimation techniques bound the influence of highly deviating observations (outliers) by using a specific bounded score function called Huber's Ψ-function that effectively gives smaller weight to observations that are outliers and have a large influence on the resulting estimate. The M-estimation methods typically require high complexity iterative computations as well as estimation of nuisance parameters such as scale in a robust manner.
Non-parametric statistical procedures make no assumptions on the distribution family. Further background to the teachings presented herein may be seen at the following references: S. A. Kassam, “SIGNAL DETECTION IN NON-GAUSSIAN NOISE” (Springer-Verlag, 1988): S. Visuri, V. Koivunen, H. Oja, entitled “SIGN AND RANK COVARIANCE MATRICES” (Journal of Statistical Planning and Inference, Vol. 91, No. 2, pp. 557-575, 2000); and W. A. Gardner, R. S. Roberts, “ONE-BIT SPECTRAL CORRELATION ALGORITHMS” IEEE Transactions on Signal Processing, Vol. 41. No 1. pp, 423-427, 1993.